Titlebook: Data Management, Analytics and Innovation; Proceedings of ICDMA Saptarsi Goswami,Inderjit Singh Barara,Alfred M. B Conference proceedings 2

invigorating

書目名稱Data Management, Analytics and Innovation影響因子(影響力)




書目名稱Data Management, Analytics and Innovation影響因子(影響力)學科排名




書目名稱Data Management, Analytics and Innovation網(wǎng)絡公開度




書目名稱Data Management, Analytics and Innovation網(wǎng)絡公開度學科排名




書目名稱Data Management, Analytics and Innovation被引頻次




書目名稱Data Management, Analytics and Innovation被引頻次學科排名




書目名稱Data Management, Analytics and Innovation年度引用




書目名稱Data Management, Analytics and Innovation年度引用學科排名




書目名稱Data Management, Analytics and Innovation讀者反饋




書目名稱Data Management, Analytics and Innovation讀者反饋學科排名

Commodious

Automated Structured Data Extraction from Scanned Document Imagestructured format. The solution is driven by a configuration file, which can help in fine-tuning different processes to improve extracted data. The solution generates an XML for the scanned document which can be used further for storing and processing the data present in paper-based documents by diff

制造

灌溉

Itinerant

Hypothesis Testing of Tweet Text Using NLPs. Tweets are labeled as “believer” or “denier” for each country, and a hypothesis is being proved based on the statement made by rich and poor countries. The statistical result also shows that there exists a positive correlation between the GDP growth rate and the number of deniers and believers in

ectropion

ectropion

Ontology-Driven Scientific Literature Classification Using Clustering and Self-supervised Learningaper, we propose an ontology-driven classification technique based on zero-shot learning in conjunction with agglomerative clustering to automatically label a scientific literature dataset related to CE and CS. We further study and compare the effectiveness of multiple text classifiers such as logis

ANN

Modeling and Forecasting Tuberculosis Cases Using Machine Learning and Deep Learning Approaches: A C CNN-LSTM Hybrid and MLP networks achieved the lowest forecasting errors compared to the other models and were chosen for forecasting pulmonary negative, positive, and TB incidence cases from 2020 to 2029. The forecasting results revealed that there would be 117.861557 new pulmonary negative inciden

Vsd168

變化

Support Vector Machines and Random Forest Classification Models for Identification of Stability in Eon modes. We also used the Synthetic Minority Oversampling Technique (SMOTE) to handle data imbalance. Our simulation results indicate that prediction of stability/instability classes for different process parameters can be achieved with high degree of confidence with robust machine learning models.